{"id":"W2952172018","doi":"10.48550/arxiv.1712.05796","title":"A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Economic and Social Research Council","keywords":"Earnings; Wage; Work (physics); Amazon rainforest; Task (project management); Labour economics; Yield (engineering); Distribution (mathematics); Economics; Business; Demographic economics; Engineering; Management; Mathematics; Accounting","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001500792,0.0006028116,0.0005033214,0.00163656,0.0006427017,0.000928212,0.000701749,0.0005571083,0.00151031],"category_scores_gemma":[0.006887181,0.0002156009,0.0004948914,0.0021898,0.0002566952,0.0006700762,0.0008029738,0.000575317,0.001952345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000571975,"about_ca_system_score_gemma":0.0006248712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01307063,"about_ca_topic_score_gemma":0.01954882,"domain_scores_codex":[0.99831,0.0004292926,0.0001237575,0.0003784619,0.0005498805,0.0002086162],"domain_scores_gemma":[0.996299,0.001303481,0.000401789,0.0004344792,0.001202063,0.0003592908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002705995,0.000855531,0.7210436,0.0007823313,0.0005344411,0.001548632,0.003176877,0.0196622,0.01029297,0.003320128,0.08641342,0.1496638],"study_design_scores_gemma":[0.00007366351,0.000436518,0.844865,0.00007823916,0.00007069775,0.0005252566,0.003178225,0.1132105,0.003277198,0.003250529,0.03090597,0.0001283427],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9487633,0.0003621366,0.005418383,0.0009166172,0.0001388642,0.0002192304,0.03896799,0.0006088828,0.00460454],"genre_scores_gemma":[0.9629721,0.000129345,0.008008084,0.00013752,0.00007237185,0.000226654,0.0256533,0.00005975592,0.002740829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01307063,"threshold_uncertainty_score":0.02598906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1037314949401206,"score_gpt":0.2248979385926566,"score_spread":0.121166443652536,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}